scholarly journals OpenWeedLocator (OWL): an open-source, low-cost device for fallow weed detection

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Guy Coleman ◽  
William Salter ◽  
Michael Walsh
Keyword(s):  
Open Source ◽  
Weed Control ◽  
Technology Development ◽  
Low Cost ◽  
Yield Potential ◽  
Low Density ◽  
Weed Detection ◽  
Site Specific ◽  
South Wales ◽  
Control Community

AbstractThe use of a fallow phase is an important tool for maximizing crop yield potential in moisture limited agricultural environments, with a focus on removing weeds to optimize fallow efficiency. Repeated whole field herbicide treatments to control low-density weed populations is expensive and wasteful. Site-specific herbicide applications to low-density fallow weed populations is currently facilitated by proprietary, sensor-based spray booms. The use of image analysis for fallow weed detection is an opportunity to develop a system with potential for in-crop weed recognition. Here we present OpenWeedLocator (OWL), an open-source, low-cost and image-based device for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive GitHub repository was developed, promoting community engagement with site-specific weed control methods. Validation of OWL as a low-cost tool was achieved using four, existing colour-based algorithms over seven fallow fields in New South Wales, Australia. The four algorithms were similarly effective in detecting weeds with average precision of 79% and recall of 52%. In individual transects up to 92% precision and 74% recall indicate the performance potential of OWL in fallow fields. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development in agriculture.

scholarly journals OpenWeedLocator (OWL): an open-source, low-cost 1 device for fallow weed detection.

2021 ◽  
Author(s):  
Guy Coleman ◽  
William Salter ◽  
Michael Walsh
Keyword(s):  
Open Source ◽  
Weed Control ◽  
Technology Development ◽  
Low Cost ◽  
Yield Potential ◽  
Low Density ◽  
Weed Detection ◽  
Living Plant ◽  
Detection Technology ◽  
Assembly Instructions

Abstract The use of a fallow phase is an important tool for maximizing yield potential in moisture limited environments. There is a focus on ensuring these phases are maintained weed-free as even low weed densities can be detrimental to fallow efficiency. Repeated whole field herbicide treatment to control low-density weed populations is expensive and wasteful. Site-specific application of herbicide treatments to low density fallow weed populations is currently facilitated by sensor-based devices that detect chlorophyll fluorescence from living plant tissue. The use of image-based weed detection technology for fallow weed detection is an opportunity to develop an approach that can be translated for in-crop weed recognition. Here we present the OpenWeedLocator (OWL), an open-source, low-cost image-based approach for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive repository, containing all code and assembly instructions, has been developed that will allow for community driven improvement over time. Four different colour-based weed detection algorithms were tested with the OWL system over seven fallow field scenarios under varying light, soil and stubble conditions. Across all scenarios, the four algorithms were similarly effective in detecting fallow weeds with average precision and recall of 79% and 52%, respectively. In individual transects, precision and recall values of up to 92% and 74%, respectively, suggest the potential fallow weed detection performance of the colour-based system. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development and implementation in the weed control industry.

scholarly journals Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

2021 ◽  
Vol 13 (10) ◽  
pp. 1869
Author(s):  
Pietro Mattivi ◽  
Salvatore Eugenio Pappalardo ◽  
Nebojša Nikolić ◽  
Luca Mandolesi ◽  
Antonio Persichetti ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Weed Management ◽  
Low Cost ◽  
Medium Size ◽  
Spatially Explicit ◽  
Weed Detection ◽  
Management Scenario ◽  
Weed Mapping ◽  
Weed Distribution

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.

scholarly journals Accuracy and Efficiency Comparison of Object Detection Open-Source Models

2021 ◽  
Vol 17 (05) ◽  
pp. 165
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Khalid Rahmani
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Large Scale ◽  
Performance Comparison ◽  
Yield Potential ◽  
Temperate Climate ◽  
Weed Species ◽  
Weed Detection ◽  
Cloudy Weather

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language.

scholarly journals Definition of Optimal Maize Seeding Rates Based on the Potential Yield of Management Zones

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 911
Author(s):  
Adriano Adelcino Anselmi ◽  
José Paulo Molin ◽  
Helizani Couto Bazame ◽  
Lucas de Paula Corrêdo
Keyword(s):  
Low Cost ◽  
Plant Population ◽  
Yield Potential ◽  
Specific Yield ◽  
Population Densities ◽  
Potential Yield ◽  
Site Specific ◽  
Management Zones ◽  
Soil Attributes ◽  
Maize Hybrids

The decision on crop population density should be a function of biotic and abiotic field parameters and optimize the site-specific yield potential, which can be a real challenge for farmers. The objective of this study was to investigate the yield of maize hybrids subjected to variable rate seeding (VRS) and in differentiated management zones (MZs). The experiment was conducted between 2013 and 2015 in a commercial field in the Central-West region of Brazil. First, MZ were delineated using the K-means algorithm with layers involving soil electrical conductivity, yield maps from previous years, and elevation. Seven maize hybrids at five seeding rates were evaluated in the context of each MZ and the cause-and-effect relationship with soil attributes was investigated. Optimal yields were obtained for crop population densities between 70,000 plants ha−1 and 80,000 plants ha−1. Hybrids which perform well under higher densities are key in achieving positive results using VRS. The plant population densities that resulted in maximum yields were obtained for densities at least 27% higher than the recommended seeding rates. The yield variance between MZs can be explained by the variance in soil attributes, while the yield variance within MZs can be explained by the variance in plant population densities. The study shows that on-farm experimentation can be key for obtaining information concerning yield potential. The management by VRS in different MZs is a low-cost technique that can reduce input application costs and optimize yield according to the site-specific potential of the field.

scholarly journals Site and time-specific early weed control is able to reduce herbicide use in maize - a case study

2021 ◽  
Author(s):  
Nebojša Nikolić ◽  
Davide Rizzo ◽  
Elisa Marraccini ◽  
Alicia Ayerdi Gotor ◽  
Pietro Mattivi ◽  
...  
Keyword(s):  
Weed Control ◽  
Field Studies ◽  
Visible Range ◽  
Weed Species ◽  
Weed Detection ◽  
Site Specific ◽  
Direct Benefits ◽  
Artificial Neural Network Ann ◽  
Time Specific ◽  
Herbicide Use

Highlights- Efficacy of UAVs and emergence predictive models for weed control has been confirmed. - Combination of time-specific and site-specific weed control provides optimal results.- Use of timely prescription maps can substantially reduce herbicide use.   Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.

Tillage based, site-specific weed control for conservation cropping systems

2020 ◽  
Vol 34 (5) ◽  
pp. 704-710
Author(s):  
Michael J. Walsh ◽  
Caleb C. Squires ◽  
Guy R. Y. Coleman ◽  
Michael J. Widderick ◽  
Adam B. McKiernan ◽  
...  
Keyword(s):  
Weed Control ◽  
Crop Production ◽  
Large Scale ◽  
Cropping Systems ◽  
Small Scale ◽  
Growth Stages ◽  
Weed Detection ◽  
Site Specific ◽  
Detection Systems ◽  
Herbicide Treatments

AbstractAustralian conservation cropping systems are practiced on very large farms (approximately 3,000 ha) where herbicides are relied on for effective and timely weed control. In many fields, though, there are low weed densities (e.g., <1.0 plant 10 m−2) and whole-field herbicide treatments are wasteful. For fallow weed control, commercially available weed detection systems provide the opportunity for site-specific herbicide treatments, removing the need for whole-field treatment of fallow fields with low weed densities. Concern about the sustainability of herbicide-reliant weed management systems remain and there has not been interest in the use of weed detection systems for alternative weed control technologies, such as targeted tillage. In this paper, we discuss the use of a targeted tillage technique for site-specific weed control in large-scale crop production systems. Three small-scale prototypes were used for engineering and weed control efficacy testing across a range of species and growth stages. With confidence established in the design approach and a demonstrated 100% weed-control potential, a 6-m wide pre-commercial prototype, the “Weed Chipper,” was built incorporating commercially available weed-detection cameras for practical field-scale evaluation. This testing confirmed very high (90%) weed control efficacies and associated low levels (1.8%) of soil disturbance where the weed density was fewer than 1.0 plant 10 m−2 in a commercial fallow. These data established the suitability of this mechanical approach to weed control for conservation cropping systems. The development of targeted tillage for fallow weed control represents the introduction of site-specific, nonchemical weed control for conservation cropping systems.

Rapid mapping accuracy assessment using low-cost UAV and open-source Structure from Motion

2020 ◽  
Vol 52 ◽  
pp. 55-61
Author(s):  
Ettore Potente ◽  
Cosimo Cagnazzo ◽  
Alessandro Deodati ◽  
Giuseppe Mastronuzzi
Keyword(s):  
Open Source ◽  
Structure From Motion ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Mapping Accuracy ◽  
Rapid Mapping ◽  
Source Structure

To Eliminate Curtain Effect of FIB TEM Samples by a Combination of Sample Dicing and Backside Milling

2014 ◽  
Author(s):  
Jian-Shing Luo ◽  
Hsiu Ting Lee
Keyword(s):  
Sample Preparation ◽  
Focused Ion Beam ◽  
Preparation Method ◽  
Low Cost ◽  
Ion Beam ◽  
Sample Preparation Method ◽  
Site Specific ◽  
Dual Beam ◽  
Transmission Electron

Abstract Several methods are used to invert samples 180 deg in a dual beam focused ion beam (FIB) system for backside milling by a specific in-situ lift out system or stages. However, most of those methods occupied too much time on FIB systems or requires a specific in-situ lift out system. This paper provides a novel transmission electron microscopy (TEM) sample preparation method to eliminate the curtain effect completely by a combination of backside milling and sample dicing with low cost and less FIB time. The procedures of the TEM pre-thinned sample preparation method using a combination of sample dicing and backside milling are described step by step. From the analysis results, the method has applied successfully to eliminate the curtain effect of dual beam FIB TEM samples for both random and site specific addresses.

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

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